{"id":"W4407689898","doi":"10.1038/s41467-025-56893-9","title":"Virtual fragment screening for DNA repair inhibitors in vast chemical space","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Cancer therapeutics and mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science for Life Laboratory; Uppsala Universitet; Vetenskapsrådet; Kungliga Tekniska Högskolan; Knut och Alice Wallenbergs Stiftelse; European Commission; Cancerfonden; European Federation of Pharmaceutical Industries and Associations; Diamond Light Source; McGill University","keywords":"Chemical space; Virtual screening; Drug discovery; Fragment (logic); Computational biology; DOCK; Chemical library; Small molecule; Docking (animal); Combinatorial chemistry; Computer science; DNA; Chemistry; Biology; Bioinformatics; Biochemistry; Algorithm; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007610096,0.0009088442,0.001305032,0.001014022,0.0003120176,0.0009239009,0.000917999,0.0004414944,0.002600577],"category_scores_gemma":[0.001106277,0.0002469968,0.0006923618,0.0009486204,0.0004181935,0.0005353599,0.0008581844,0.0004620262,0.0003806238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000662875,"about_ca_system_score_gemma":0.0007059829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001034665,"about_ca_topic_score_gemma":0.001555828,"domain_scores_codex":[0.9996469,0.0001083511,0.00001624302,0.00005062418,0.0001255412,0.00005248241],"domain_scores_gemma":[0.9995756,0.000243447,0.00005384437,0.00003713747,0.00004827761,0.00004170603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002811647,0.0007885154,0.0042904,0.0004989944,0.0003558028,0.0003740663,0.00005906708,0.7459065,0.1539151,0.005061395,0.001521252,0.08441726],"study_design_scores_gemma":[0.0006887648,0.007064654,0.002210569,0.00004252077,0.000313674,0.000447025,0.0001010756,0.8403752,0.138593,0.00494277,0.005132078,0.00008874114],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9248394,0.002966744,0.06254891,0.0002236154,0.00004208626,0.0003116811,0.001204466,0.0019275,0.005935502],"genre_scores_gemma":[0.9713876,0.0008114903,0.02548042,0.000101456,0.00001207888,0.0001607066,0.001034782,0.00006071615,0.0009508183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002600577,"threshold_uncertainty_score":0.008699834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01440605193206448,"score_gpt":0.3088082304449167,"score_spread":0.2944021785128522,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}